The exemplary embodiment relates to digital image collections. It finds particular application in connection with a system and method for playing a game in which color palettes are matched to photographic images, which can assist a user in making digital image or color palette selections.
Photographic images are often collected and stored in large databases with tags which describe their semantic content, such as “sky,” or “flower,” or their aesthetic features, such as “happy,” “funny,” and the like. Such databases are accessed by a variety of users searching for suitable images to meet a particular need. The typical user of an image retrieval system has been characterized as falling into one of three profiles: a “browser” is a user with no clear end-goal, who often jumps from one topic to another; a “surfer” is a user with an initial exploratory behavior that clarifies his or her goals through multiple queries; and a “searcher” is a user with very clear goal-regarding semantic and aesthetic features of the image for which he or she is looking. See Datta, R., Joshi, D., Li, J., and Wang, J. Z; “Image retrieval: Ideas, influences, and trends of the new age,” ACM Comput. Surv. 40:2, 1-60 (April 2008) (hereinafter “Datta 2008”). Recent studies performed among graphic designers and photo editors show that such users, in most cases, would fall into one of the latter two profiles. It has also been observed that the activity of browsing through a large collection of images is often used to define and refine the criteria of the search itself.
Although image retrieval systems are often equipped with tools to support searchers in their targeted retrieval workflows, less attention is dedicated to develop methods for exploratory image search performed by both browsers and searchers. While some systems are specifically designed for this kind of user experience, they are based on random slideshows. Relevance-feedback systems could be considered for this kind of search. However, the level of user engagement in the search tends to be very limited and the act of constantly providing feedback can prove tedious for users.
Researchers in the field of psychology have shown that games can elicit mental states which are especially favorable to creation and learning activities. See Snyder, C. R. & Lopez, S. J, Positive psychology: The scientific and practical explorations of human strengths, London, UK: Sage Publications (2007). Games have been used to address computer vision problems. See, for example, von Ahn, L., and Dabbish, L., “Labeling images with a computer game,” in Proc. SIGCHI Conf. on Human Factors in Computing Systems (Vienna, Austria, Apr. 24-29, 2004), CHI '04, ACM, New York, N.Y., 319-326, which uses an interactive system for image segmentation. Other games, such as Peekaboom, ESP, and Google Image Labeler have proved useful in the generation of costly ground truth for machine learning and pattern recognition experiments (see, e.g., von Ahn, L., Liu, R., and Blum, M., “Peekaboom: a game for locating objects in images,” in Proc. SIGCHI Conf. on Human Factors in Computing Systems (Montreal, Quebec, Canada, April 22-27, 2006). R. Grinter, et al., Eds. CHI '06. ACM, New York, N.Y., 55-64). The underlying assumption is that humans require some incentive to become part of a collective task. More recent approaches also leverage outsourcing services, such as Mechanical Turks to perform such tasks.
The exemplary embodiment provides a system and method which assists users, such as graphic designers, who are commonly searching for images to be used within their creative workflows. By using the game dimension to engage the user in non targeted image searches, the beneficial effects of a game upon the creative process can be realized.